Focusing on the issue that many image denoising methods are easy to lose detailed information when removing noise, a nonlocal self-similarity based low-rank sparse image denoising method was proposed. Firstly, external natural clean image patches were put into groups by a method of block matching based on Mahalanobis Distance (MD), and then a patch group based Gaussian Mixture Model (GMM) was developed to learn the nonlocal self-similarity prior. Secondly, based on the Stable Principal Component Pursuit (SPCP) method, the noise image matrix was decomposed into low-rank, sparse and noise parts, while the sparse matrix contained useful information. Finally, the global objective function was minimized to achieve denoising. The experimental results show that compared to the previous denoising methods, such as EPLL (Expected Patch Log Likelihood), NCSR (Non-locally Centralized Sparse Representation), PCLR (external Patch prior guided internal CLusteRing), etc., the proposed method has better results in Peak Signal-to-Noise Ratio (PSNR) and Structure self-SIMilarity (SSIM), speed, denoising effect and detail retention ability.

Focusing on the person re-identification in non-overlapping camera views and the high dimensional feature extracted from the images, a person re-identification method based on block sparse representation was proposed. The Canonical Correlation Analysis (CCA) was taken to carry out the feature projection transformation, and the curse of dimensionality caused by high dimensional feature operation was avoided by improving the feature matching ability, and the feature vectors in a probe image were made to be probably linear with the corresponding gallery feature vectors in the learned projected space of CCA transformation. A person re-identification model was also built with block structure feature of pedestrian dataset, and the associated optimization problem was solved by utilizing the alternating direction framework. Finally, the residues were used to deal with the person in the probe set to be identified and the index of the minimum value in the residues was regarded as the identity of the person. Several experiments were conducted on public datasets such as PRID 2011, iLIDS-VID and VIPeR. The experimental results show that the Rank1 value of the proposed method on three experimental datasets reaches 40.4%, 38.11% and 23.68%, respectively, which is significantly higher than that of Large Margin Nearest Neighbor (LMNN) method, and the matching rate of it on Rank-1 is also much bigger than that of LMNN method; besides, the overall performance of it is better than the classical algorithms based on feature representation and metric learning. The experimental results verify the effectiveness of the proposed method on person re-identification.